Currently, untrusted third-party entities are increasingly involved in various stages of IC design and manufacturing, posing a significant threat to the reliability and security of SoCs due to the presence of hardware Trojans (HTs). In this paper, gate-level HT detection methods based on graph neural networks (GNNs) are established to overcome the defects of existing machine learning, which makes it difficult to characterize circuit connection relationships. We introduce harmonic centrality in the feature engineering of gate-level HT detection, which reflects the positional information of nodes and their adjacent nodes in the graph, thereby enhancing the quality of feature engineering. We use the golden section weight optimization algorithm to configure penalty weights to alleviate the problem of extreme data imbalance. In the SAED database, GraphSAGE-LSTM model obtained a TPR of 88.06% and an average F1 score of 90.95%. In the combined HT netlist of LEDA datasets, GraphSAGE-POOL model obtains a TPR of 88.50% and the best F1 score of 92.17%. In sequential HT netlist, GraphSAGE-LSTM model performs optimally, with a TPR of 98.25% and an average F1 score of 98.59%. Compared to existing detection models, the F1 score is enhanced by 8.86% and 2.48% on combined and sequential HT datasets, respectively.

The existing hardware Trojan (HT) detection technology usually relies on the golden reference model. With the continuous improvement of circuit integration, the detection accuracy of traditional methods such as side-channel analysis has declined. Methods based on testability and switch probability analysis have shown high detection accuracy. However, these techniques have a limited detection scope and are generally ineffective at identifying HT where the Sandia controllability/observability analysis program (SCOAP) values or switch probabilities are similar to those of normal signals. Against this backdrop, this article proposes a detection method based on graph neural networks (GNNs), which can achieve HT detection at the register transfer level (RTL) without the golden reference model. First, the RTL code is transformed into a data flow graph (DFG), and node feature extraction and node label marking are carried out during the transformation process. To mitigate the impact of insufficient initial features on the GNN model performance, the node feature vector used in this article comprises 37-D node types and 6-D structural features such as the minimum distance from the primary input (PI) and primary output (PO), in-degree, and out-degree. Subsequently, several GNN models are built for node classification tasks. The best model achieves an average of 99.1% recall and 96.7% F1-score on the open-source dataset on the Trust-Hub platform. Compared to the state-of-the-art detection results at RTL, the F1-score in this article has increased by an average of 3.8%.

Presents corrections to the paper, (Corrections to “GNN-Based Hardware Trojan Detection at Register Transfer Level Leveraging Multiple-Category Features”).

Current image compression algorithms based on transforms can achieve ideal performance for natural images, but do not do well with synthetic aperture radar (SAR) images. We propose a learned compression framework with pyramidal features and quality enhancement to fully exploit the redundancy among image pixels and to improve the compression bitrate and reconstruction quality. Based on the variational autoencoder (VAE) architecture, pyramidal decomposition is performed at the first autoencoder to extract both global and coarse feature maps. The latent distribution is modeled by the second hyperprior autoencoder with a single-Gaussian model for more accurate and flexible entropy estimation. Universal quantization is applied to consolidate the entropy estimation accuracy of the hyperprior network. To further improve reconstruction quality, a residual dense network (RDN) is adopted to fully capture local and global features. Experimental results demonstrate that the proposed framework provides a better rate-distortion tradeoff than standard codecs such as JPEG, JPEG2000, and learning-based methods on both the Sandia and ICEYE datasets.

Given the uniqueness of synthetic aperture radar (SAR) images, traditional optical image compression algorithms cannot fully exploit their redundant information. To improve SAR image compression in terms of rate–distortion performance and visual perception, an end-to-end SAR image compression convolutional neural network (CNN) model based on a variational autoencoder is proposed. The proposed CNN model consists of a main autoencoder and a hyper autoencoder. To reduce dependencies in latent space, a joint transform of linear CNN and nonlinear generalized divisive normalization (GDN) activation is applied in the main autoencoder. Moreover, residual blocks are combined with the transforms to boost the efficiency of feature learning and make use of subpixels to improve the quality of reconstructed images. Instead of a fixed entropy model, a conditioned entropy model that works with a hyperprior network is used to learn the distribution of latents, which helps to further improve the compression quality. During training, the model is optimized by evaluating the rate–distortion performance. The experimental results show that the proposed method can achieve better distortion performance than JPEG, JPEG2000, and the available CNN-based method in terms of objective evaluation criteria and human vision perception quality.

With the increasing complexity of modern design, mixed-cell-height designs have become more popular, which makes the legalization problem more challenging. In this article, a robust negotiation-based legalizer (NBLG) is proposed to reduce average displacement and maximum displacement for mixed-cell-height circuits with considering the fence region and technology constraints. By dissecting the main components of the negotiation-based method, we divide the placement grid in terms of placement sites and reformulate the legalization problem as a resource allocation task. We then allow all movable cells to gradually remove overlaps in a surrounding window with two individual techniques: 1) isolation point and 2) adaptive penalty function. We also adopt a deterministic multithreading technique to accelerate the convergence of our algorithm. Experimental results show that our legalizer achieved the minimal average displacement and maximum displacement in a reasonable runtime compared with state-of-the-art methods.